OpenRaiser/Intern-Atlas huggingface dataset:
Don't be lazy: CompleteP enables compute-efficient deep transformers
Venue: arXiv cs.LG 2026
Year: 2026

We study compute efficiency of LLM training when using different parameterizations, i.e., rules for adjusting model and optimizer hyperparameters (HPs) as model size changes. Some parameterizations fail to transfer optimal base HPs (such as learning rate) across changes in model depth, requiring practitioners to either re-tune these HPs as they scale up (expensive), or accept sub-optimal training when re-tuning is prohibitive. Even when they achieve HP transfer, we develop theory to show parameterizations may still exist in the lazy learning regime where layers learn only features close to their linearization, preventing effective use of depth and nonlinearity. Finally, we identify and adopt the parameterization we call CompleteP that achieves both depth-wise HP transfer and non-lazy learning in all layers. CompleteP enables a wider range of model width/depth ratios to remain compute-efficient, unlocking shapes better suited for different hardware settings and operational contexts. Moreover, CompleteP enables 12-34% compute efficiency improvements over the prior state-of-the-art. All experiments were run on Cerebras CS-3 systems. A minimal implementation is available at https://github.com/EleutherAI/nanoGPT-mup/tree/completep.
Semantics-Aware Unified Terrestrial Non-Terrestrial 6G Networks
Venue: arXiv cs.NI 2026
Year: 2026

The integration of Terrestrial and Non-Terrestrial Networks (TN-NTNs), introduced in 5G, is advancing toward a unified and seamless network of networks in Sixth-Generation (6G). This evolution markedly increases the volume of generated and exchanged data, imposing stringent technical and operational requirements along with higher cost and energy consumption. Consequently, efficient management of data generation and transmission within this unified architecture has become essential. In this article, we investigate semantics-aware information handling in unified TN-NTNs, where data communication between distant TN nodes is enabled via an NTN. We consider an Internet of Things (IoT) monitoring system in which status updates from a remote Energy Harvesting (EH) device are delivered to a destination monitor through a network of Low Earth Orbit (LEO) satellites. We leverage semantic metrics, such as Query Version Age of Information, which collectively capture the timeliness, relevance, and utility of information. This approach minimizes the transmission of stale, uninformative, or unusable information, thereby reducing the volume of data that must be transmitted and processed. The result is a substantial reduction in energy consumption and data exchange within the network-achieving up to 73% lower energy-charging requirements and fewer transmission demands than the state of the art-without compromising the conveyed information.
DiTOX: Fault Detection and Localization in the ONNX Optimizer
Venue: arXiv cs.LG 2026
Year: 2026

The ONNX Optimizer, part of the official ONNX repository and widely adopted for graph-level model optimizations, is used by default to optimize ONNX models. Despite its popularity, its ability to preserve model correctness has not been systematically evaluated. We present DiTOX, an automated framework for comprehensively assessing the correctness of the ONNX Optimizer using differential testing, fault localization, and evaluation techniques that generalize to other compiler optimizers. DiTOX applies optimization passes to a corpus of ONNX models, executes both original and optimized versions on user-defined inputs, and detects discrepancies in behavior or optimizer failures. When divergences are observed, DiTOX isolates the responsible optimization pass through iterative, fine-grained analysis. We evaluated DiTOX on 130 models from the ONNX Model Hub spanning vision and language tasks. We found that 9.2% of model instances crashed the optimizer or produced invalid models under default settings. Moreover, output discrepancies occurred in 30% of classification models and 16.6% of object detection and segmentation models, while text-based models were largely robust. Overall, DiTOX uncovered 15 issues -- 14 previously unknown -- affecting 9 of the 47 optimization passes as well as the optimizer infrastructure. All issues were reported to the ONNX Optimizer developers. Our results demonstrate that DiTOX provides a simple and effective approach for validating AI model optimizers and is readily extensible beyond ONNX.
RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video
Venue: arXiv cs.CV 2026
Year: 2026

Multimodal Large Language Models (MLLMs) have made rapid progress in perception, understanding, and reasoning, yet existing benchmarks fall short in evaluating these abilities under continuous and dynamic real-world video streams. Such settings require models to maintain coherent understanding and reasoning as visual scenes evolve over time. **We introduce RTV-Bench, a fine-grained benchmark for real-time video analysis with MLLMs**. It is built upon three key principles: multi-timestamp question answering, hierarchical question structures spanning perception and reasoning, and multi-dimensional evaluation of continuous perception, understanding, and reasoning. RTV-Bench comprises 552 diverse videos and 4,608 carefully curated QA pairs covering a wide range of dynamic scenarios. We evaluate a broad range of state-of-the-art MLLMs, including proprietary, open-source offline, and open-source real-time models. Our results show that real-time models generally outperform offline counterparts but still lag behind leading proprietary systems. While scaling model capacity generally yields performance gains, simply increasing the density of sampled input frames does not consistently translate into improved results. These observations suggest inherent limitations in current architectures when handling long-horizon video streams, underscoring the need for models explicitly designed for streaming video processing and analysis.
RM-R1: Reward Modeling as Reasoning
Venue: arXiv cs.CL 2026
Year: 2026

Reward modeling is essential for aligning large language models with human preferences through reinforcement learning. To provide accurate reward signals, a reward model (RM) should stimulate deep thinking and conduct interpretable reasoning before assigning a score or a judgment. Inspired by recent advances of long chain-of-thought on reasoning-intensive tasks, we hypothesize and validate that integrating reasoning into reward modeling significantly enhances RM's interpretability and performance. We introduce a new class of generative reward models, Reasoning Reward Models (ReasRMs), which formulate reward modeling as a reasoning task. We propose a reasoning-oriented training pipeline and train a family of ReasRMs, RM-R1. RM-R1 features a chain-of-rubrics (CoR) mechanism -- self-generating sample-level chat rubrics or math/code solutions, and evaluating candidate responses against them. The training of RM-R1 consists of two key stages: (1) distillation of high-quality reasoning chains and (2) reinforcement learning with verifiable rewards. Empirically, our models achieve superior performance across three reward model benchmarks on average, outperforming much larger open-weight models (e.g., INF-ORM-Llama3.1-70B) and proprietary ones (e.g., GPT-4o) by up to 4.9%. Beyond final performance, we perform thorough analyses to understand the key ingredients of successful ReasRM training.
Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities
Venue: arXiv cs.CV 2026
Year: 2026

Recent years have seen remarkable progress in both multimodal understanding models and image generation models. Despite their respective successes, these two domains have evolved independently, leading to distinct architectural paradigms: While autoregressive-based architectures have dominated multimodal understanding, diffusion-based models have become the cornerstone of image generation. Recently, there has been growing interest in developing unified frameworks that integrate these tasks. The emergence of GPT-4o's new capabilities exemplifies this trend, highlighting the potential for unification. However, the architectural differences between the two domains pose significant challenges. To provide a clear overview of current efforts toward unification, we present a comprehensive survey aimed at guiding future research. First, we introduce the foundational concepts and recent advancements in multimodal understanding and text-to-image generation models. Next, we review existing unified models, categorizing them into three main architectural paradigms: diffusion-based, autoregressive-based, and hybrid approaches that fuse autoregressive and diffusion mechanisms. For each category, we analyze the structural designs and innovations introduced by related works. Additionally, we compile datasets and benchmarks tailored for unified models, offering resources for future exploration. Finally, we discuss the key challenges facing this nascent field, including tokenization strategy, cross-modal attention, and data. As this area is still in its early stages, we anticipate rapid advancements and will regularly update this survey. Our goal is to inspire further research and provide a valuable reference for the community. The references associated with this survey are available on GitHub (https://github.com/AIDC-AI/Awesome-Unified-Multimodal-Models).
Decoding Open-Ended Information Seeking Goals from Eye Movements in Reading
Venue: arXiv cs.CL 2026
Year: 2026

When reading, we often have specific information that interests us in a text. For example, you might be reading this paper because you are curious about LLMs for eye movements in reading, the experimental design, or perhaps you wonder ``This sounds like science fiction. Does it actually work?''. More broadly, in daily life, people approach texts with any number of text-specific goals that guide their reading behavior. In this work, we ask, for the first time, whether open-ended reading goals can be automatically decoded solely from eye movements in reading. To address this question, we introduce goal decoding tasks and evaluation frameworks using large-scale eye tracking for reading data in English with hundreds of text-specific information seeking tasks. We develop and compare several discriminative and generative multimodal text and eye movements LLMs for these tasks. Our experiments show considerable success on the task of selecting the correct goal among several options, and even progress towards free-form textual reconstruction of the precise goal formulation. These results open the door for further scientific investigation of goal driven reading, as well as the development of educational and assistive technologies that will rely on real-time decoding of reader goals from their eye movements.
Unveiling the Role of ChatGPT in Software Development: Insights from Developer-ChatGPT Interactions on GitHub
Venue: arXiv cs.SE 2026
Year: 2026

The advent of Large Language Models (LLMs) has introduced a new paradigm in Software Engineering (SE), with generative AI tools like ChatGPT gaining widespread adoption among developers. While ChatGPT's potential has been extensively discussed, empirical evidence about how developers actually use LLMs' assistance in real-world practices remains limited. To bridge this gap, we conducted a large-scale empirical analysis of ChatGPT usage on GitHub, and we presented DevChat, a curated dataset of 2,547 publicly shared ChatGPT conversation links collected from GitHub between May 2023 and June 2024. Through comprehensively analyzing DevChat, we explored the characteristics of developer-ChatGPT interaction patterns and identified five key categories of developers' purposes for sharing developer-ChatGPT conversations during software development. Additionally, we investigated the dominant development-related activities in which ChatGPT is used, and presented a mapping framework that links GitHub data sources, development-related activities, and SE tasks. The findings show that interactions are typically short and task-focused (most are 1-3 turns); developers share conversations mainly to delegate tasks, resolve problems, and acquire knowledge, revealing five purpose categories; ChatGPT is most frequently engaged for Software Implementation and Maintenance & Evolution; we identified 39 fine-grained SE tasks supported by ChatGPT, with Code Generation & Completion as well as Code modification & Optimization being the most prominent. Our study offers a comprehensive mapping of ChatGPT's applications in real-world software development scenarios and provides a foundation for understanding LLMs' practical roles in software development.
Secret Sharing Schemes from Correlated Random Variables and Rate-Limited Public Communication
Venue: arXiv cs.IT 2026
Year: 2026

A dealer aims to share a secret with participants so that only predefined subsets can reconstruct it, while others learn nothing. The dealer and participants access correlated randomness and communicate over a one-way, public, rate-limited channel. For this problem, we propose the first explicit coding scheme able to handle arbitrary access structures and achieve the best known achievable rates, previously obtained non-constructively. Our construction relies on lossy source coding coupled with distribution approximation to handle the reliability constraints, followed by universal hashing to handle the security constraints. We stress that our coding scheme does not require symmetry or degradation assumptions on the correlated random variables, and does not need a pre-shared secret among the participants and dealer. As a by-product, our construction also yields explicit coding schemes for secret-key generation under one-way, rate-limited public communication that, unlike prior work, achieves the capacity for arbitrary source correlations and do not require a pre-shared secret to ensure strong secrecy.
Riemannian Denoising Diffusion Probabilistic Models
Venue: arXiv cs.LG 2026
Year: 2026

We propose Riemannian Denoising Diffusion Probabilistic Models (RDDPMs) for learning distributions on submanifolds of Euclidean space that are level sets of functions, including most of the manifolds relevant to applications. Existing methods for generative modeling on manifolds rely on substantial geometric information such as geodesic curves or eigenfunctions of the Laplace-Beltrami operator and, as a result, they are limited to manifolds where such information is available. In contrast, our method, built on a projection scheme, can be applied to more general manifolds, as it only requires being able to evaluate the value and the first order derivatives of the function that defines the submanifold. We provide a theoretical analysis of our method in the continuous-time limit, which elucidates the connection between our RDDPMs and score-based generative models on manifolds. The capability of our method is demonstrated on datasets from previous studies and on new datasets sampled from two high-dimensional manifolds, i.e. $\mathrm{SO}(10)$ and the configuration space of molecular system alanine dipeptide with fixed dihedral angle.
Optimal Deterministic Rendezvous in Labeled Lines
Venue: arXiv cs.DC 2026
Year: 2026

In a rendezvous task, some mobile agents dispersed in a network have to gather at an arbitrary common site. We consider the rendezvous problem on the infinite labeled line, with $2$ agents, without communication, and a synchronous notion of time. Each node on the line is labeled with a unique positive integer. The initial distance between the agents is denoted by $D$. Time is divided into rounds and measured from the moment an agent first wakes up. We denote by $τ$ the delay between the two agents' wake up times. If awake in a given round $T$, an agent at a node $v$ has three options: stay at the node $v$, take port $0$, or take port $1$. If it decides to stay, the agent will still be at node $v$ in round $T+1$. Otherwise, it will be at one of the two neighbors of $v$ on the infinite line, depending on the port it chose. The agents achieve rendezvous in $T$ rounds if they are at the same node in round $T$. We aim for a deterministic algorithm for this problem. The problem was recently considered by Miller and Pelc [Distributed Computing 2025]. With $\ell_{\max}$ the largest label of the two starting nodes, they showed that no algorithm can guarantee rendezvous in $o(D \log^* \ell_{\max})$ rounds. The lower bound follows from a connection with the LOCAL model of distributed computing, and holds even if the agents are guaranteed simultaneous wake-up ($τ= 0$) and are told their initial distance $D$. Miller and Pelc also gave an algorithm of optimal matching complexity $O(D \log^* \ell_{\max})$ when the agents know $D$, but only obtained the higher bound of $O(D^2 (\log^* \ell_{\max})^3)$ when $D$ is unknown to the agents. We improve this complexity to a tight $O(D \log^* \ell_{\max})$. In fact, our algorithm achieves rendezvous in $O(D \log^* \ell_{\min})$ rounds, where $\ell_{\min}$ is the smallest label within distance $O(D)$ of the two starting positions.
HEXGEN-FLOW: Optimizing LLM Inference Request Scheduling for Agentic Text-to-SQL
Venue: arXiv cs.DB 2026
Year: 2026

Recent advances in agentic large language models (LLMs) have substantially improved Text-to-SQL, enabling users without database expertise to query databases intuitively. However, deploying agentic LLM-based Text-to-SQL systems in production remains challenging due to multi-stage dependencies, strict latency requirements, and deployment complexity across heterogeneous GPUs in enterprise clusters. Existing LLM serving frameworks are designed mainly for independent inference tasks, leading to suboptimal performance and frequent service-level objective (SLO) violations for Text-to-SQL workloads. In this paper, we introduce \sys, a framework for scheduling and executing agentic multi-stage LLM-based Text-to-SQL workflows on heterogeneous GPU clusters serving multi-tenant requests. \sys adopts a hierarchical scheduler that combines global workload-balanced task dispatching with an adaptive local priority queue, guided by a systematic analysis of agentic Text-to-SQL workflows. We also propose a lightweight simulation-based method to tune key scheduling hyperparameters, improving robustness and adaptability. Evaluations on realistic Text-to-SQL benchmarks show that \sys significantly outperforms state-of-the-art LLM serving frameworks. Across all traces, \sys reduces P95 tail latency by $1.42{\sim}1.56\times$ and increases throughput by $1.49{\sim}1.81\times$, demonstrating consistent gains under diverse workloads.
Performance Estimation in Binary Classification Using Calibrated Confidence
Venue: arXiv cs.LG 2026
Year: 2026

Model monitoring is a critical component of the machine learning lifecycle, safeguarding against undetected drops in the model's performance after deployment. Traditionally, performance monitoring has required access to ground truth labels, which are not always readily available. This can result in unacceptable latency or render performance monitoring altogether impossible. Recently, methods designed to estimate the accuracy of classifier models without access to labels have shown promising results. However, there are various other metrics that might be more suitable for assessing model performance in many cases. Until now, none of these important metrics has received similar interest from the scientific community. In this work, we address this gap by presenting CBPE, a novel method that can estimate any binary classification metric defined using the confusion matrix. In particular, we choose four metrics from this large family: accuracy, precision, recall, and F$_1$, to demonstrate our method. CBPE treats the elements of the confusion matrix as random variables and leverages calibrated confidence scores of the model to estimate their distributions. The desired metric is then also treated as a random variable, whose full probability distribution can be derived from the estimated confusion matrix. CBPE is shown to produce estimates that come with strong theoretical guarantees and valid confidence intervals.
LiteLMGuard: Seamless and Lightweight On-Device Prompt Filtering for Safeguarding Small Language Models against Quantization-induced Risks and Vulnerabilities
Venue: arXiv cs.CR 2026
Year: 2026

The growing adoption of Large Language Models (LLMs) has influenced the development of Small Language Models (SLMs) for on-device deployment across smartphones and edge devices, offering enhanced privacy, reduced latency, server-free functionality, and improved user experience. However, due to on-device resource constraints, SLMs undergo size optimization through compression techniques like quantization, which inadvertently introduce fairness, ethical and privacy risks. Critically, quantized SLMs may respond to harmful queries directly, without requiring adversarial manipulation, raising significant safety and trust concerns. To address this, we propose LiteLMGuard, an on-device guardrail that provides real-time, prompt-level defense for quantized SLMs. Additionally, our guardrail is designed to be model-agnostic such that it can be seamlessly integrated with any SLM, operating independently of underlying architectures. Our LiteLMGuard formalizes deep learning (DL)-based prompt filtering by leveraging semantic understanding to classify prompt answerability for SLMs. Built on our curated Answerable-or-Not dataset, LiteLMGuard employs ELECTRA as the candidate model with 97.75% answerability classification accuracy. The on-device deployment of LiteLMGuard enabled real-time offline filtering with over 85% defense-rate against harmful prompts (including jailbreak attacks), 94% filtering accuracy and ~135 ms average latency. These results demonstrate LiteLMGuard as a lightweight robust defense mechanism for effectively and efficiently securing on-device SLMs against Open Knowledge Attacks.
Behind the Byline: A Large-Scale Study of Scientific Author Contributions
Venue: arXiv cs.DL 2026
Year: 2026

Understanding how co-authors distribute credit is critical for accurately assessing scholarly collaboration. In this study, we uncover the implicit structures within scientific teamwork by systematically analyzing author contributions across a large corpus of research publications. We introduce a computational framework designed to convert free-text contribution statements into 14 standardized CRediT categories, identifying clear and consistent positional patterns in task assignments. By analyzing over 400,000 scientific articles from prominent sources such as PLOS One and Nature, we extracted and standardized more than 5.6 million author-task assignments corresponding to 1.58 million author mentions. Our analysis reveals substantial disparities in workload distribution. Notably, in small teams with three co-authors, the most engaged contributor performs over three times more tasks than the least engaged, a disparity that grows linearly with team size. This demonstrates a consistent pattern of central and peripheral roles within modern collaborative teams. Moreover, our analysis shows distinct positional biases in task allocation: technical responsibilities, such as software development and formal analysis, broadly fall to authors positioned earlier in the author list, whereas managerial tasks, including supervision and funding acquisition, increasingly concentrate among authors positioned toward the end. This gradient underscores a significant division of labor, where early-listed authors mainly undertake most hands-on activities. In contrast, senior authors mostly assume roles involving leadership and oversight. Our findings highlight the structured and hierarchical organization within scholarly collaborations, providing deeper insights into the specific roles and dynamics that govern academic teamwork